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3D scientific data visualization library and application

Project description

Mayavi docs: http://docs.enthought.com/mayavi/mayavi/TVTK docs: http://docs.enthought.com/mayavi/tvtk

Vision

Mayavi seeks to provide easy and interactive visualization of 3D data. It doesthis by the following:

• an (optional) rich user interface with dialogs to interact with all dataand objects in the visualization.
• a simple and clean scripting interface in Python, including one-liners,a-la mlab, or object-oriented programming interface.
• harnesses the power of the VTK toolkit without forcing you to learn it.

Additionally Mayavi strives to be a reusable tool that can be embedded in yourapplications in different ways or combined with the envisageapplication-building framework to assemble domain-specific tools.

Mayavi is part of the Enthought Tool Suite (ETS).

Features

Mayavi is a general purpose, cross-platform tool for 2-D and 3-D scientificdata visualization. Its features include:

• Visualization of scalar, vector and tensor data in 2 and 3 dimensions
• Easy scriptability using Python
• Easy extendability via custom sources, modules, and data filters
• Reading several file formats: VTK (legacy and XML), PLOT3D, etc.
• Saving of visualizations
• Saving rendered visualization in a variety of image formats
• Convenient functionality for rapid scientific plotting via mlab (see mlabdocumentation)
• See the Mayavi Users Guide for more information.

Unlike its predecessor MayaVi1, Mayavi has been designed with scriptabilityand extensibility in mind from the ground up. While the mayavi2 applicationis usable by itself, it may be used as an Envisage plugin which allows it tobe embedded in user applications natively. Alternatively, it may be used as avisualization engine for any application.

Quick start

If you are new to Mayavi it is a good idea to read the online user manualwhich should introduce you to how to install and use it.

If you have installed Mayavi as described in the next section, you should beable to launch the mayavi2 application and also run any of the examplesin the examples directory.

Installation

By itself Mayavi is not a difficult package to install but its dependenciesare unfortunately rather heavy. However, many of these dependencies are nowavailable as wheels on PyPI. The two critical dependencies are,

1. A GUI toolkit, either PyQt4, PySide, PySide2, PyQt5 or wxPython.

The latest VTK wheels are available on all the major platforms (Windows,MacOS, and Linux), but only for 64 bit machines. Python 3.x is fully supportedon all these operating systems and Python 2.7.x on MacOS and Linux. If you areout of luck, and your platform is not supported then you will need to installVTK yourself using your particular distribution as discussed in the GeneralBuild and Installation instructions

On Python 3.x you will need to install PyQt5 and wheels are available forthis. On 2.7.x you have more options, and can use PySide, PyQt4, andwxPython. These can be installed from pip or from your package manager.

Currently, Mayavi itself should work with the new wxPython 4.x. However,traitsui, pyface, and other ETS packages do not yet support it so the UIwill not work correctly. Older versions should work. PyQt/PySide/PySide2should work largely out of the box.

Latest stable release

As of the latest release, i.e. 4.6.0 and above, if you are using Python 3.xand are on a 64 bit machine, installation via pip is the easiest and is asfollows:

Thats it!

If you are unable to do this, read the documentation above and find a way toinstall VTK and a suitable UI toolkit and then repeat the above.

If you are interested in the jupyter notebook support as well, do thefollowing (after ensuring that you have jupyter installed of course):

You will also need to have ipywidgets and ipyevents installed. These can beinstalled via pip or your favorite package manager.

Bleeding edge

If you want to install the latest version of Mayavi from github, you cansimply do the following:

Add the jupyter nbextensions using the instructions in the section above andyou should be good to go.

Documentation

More documentation is available in the online user manual or in docsdirectory of the sources. This includes a man page for the mayavi2application, a users guide in HTML and PDF format and documentation formlab.

More documentation in the form of workshop/tutorial material is availablehere:

Tutorial Videos

Here are some tutorial videos that you can watch to learn Mayavi:

• SciPy 2018 Mayavi tutorial (3 hrs):
  • Video: https://www.youtube.com/watch?v=r6OD07Qq2mw
  • Material: https://github.com/prabhuramachandran/mayavi-tutorial

Examples

Examples are all in the examples directory of the source or the git clone.The docs and examples do not ship with the binary eggs. The examples directoryalso contains some sample data.

Test suite

The basic test suites for tvtk and mayavi can be run using nose:

The integration tests:

Bug tracker, mailing list etc.

The bug tracker is available in github Please provide info and detailson platform, python, vtk and gui backends and their versions. If possible, asmall example replicating the the problem.

If you have questions you could ask on the Mayavi-users mailing list. This is used bysome folks and is not too active. Another mailing list that may be of use isthe ETS Users mailing list. This is a more generallist where a lot of folks experienced with the Enthought Tool Suite areavailable.

Authors and Contributors

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• Core contributors:

  Prabhu Ramachandran: primary author.

• Previous contributors:

  Gaël Varoquaux: mlab, icons, many general improvements and maintenance.

  Deepak Surti: Upgrade to VTK 5.10.1, VTK 6.x with new pipeline.

• 2008 mazda 2 service manual. Support and code contributions from Enthought Inc.

• Patches from many people (see the release notes), including K K Rai andR A Ambareesha for tensor support, parametric source and image data.

  Many thanks to all those who have submitted bug reports and suggestions forfurther enhancements.

Release historyRelease notifications RSS feed

4.7.2

4.7.1

4.7.0

4.6.2

4.6.1

4.6.0

4.5.0

4.4.4

4.4.3

4.4.2

4.4.1

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4.3.1

4.3.0

4.2.0

4.1.0

4.0.0

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